MTransE | 17 paper Multilingual Knowledge Graph Embeddings | Graph Database library

I am interested in storing a set of users that have personality scores. I would like to get them to be more connected (closer?) to each other based on formulas that are applied to their scores. The more similar the users are, the more connected or closer to each other they are (like in a cluster). The closest nodes are to one-another, the more similar they are.

I currently do this over multiple steps (some in SQL and other in code) from a relational database.

Most posts out there and documentation seems to focus on how to get started and what the advantages are at a high level compared to relational databases.

I am wondering if Graph databases are better suited for this and would do most of the heavy lifting out of the box or more natively. Any details are greatly appreciated.

I am coming from graph databases and postgres is still super foreign to me.

I have the following tables

I am new to the graph databases and Gremlin. What I am trying to achieve is to ask a simple question to the database. Provided id of the Entity and id of the File, I want to find all other entites who have access to this file via the Share and return the result with the property "type" of the share. Here is the graph itself:

What I tried:

g.V("Enity:1").out("Created").hasLabel("Share").where(out("Shared").hasId("File:1")).in("Access").path()

This returns me:

I have been thinking about what are the pros and cons of SQL Graph Database and Cosmos Graph Database, as far as I understand, SQL graph database is using nodes and vertex, but it still stores all of the information in tables.

So my question would be if the graph data can be handled by graph Db, what are the advantages of using SQL graph database? What is the added value of it compared with the original graph Database ?

We use Neo4j AuraDB for our graph database but there we have issues with data upload. So, we decided to move to AWS Neptune using the migration tool.

We have 3.7M nodes and 11.2M relations in our database. The DB instance is db.r5.large with 2 CPUs and 16GiB RAM.

The same AWS Neptune OpenCypher queries are much slower than AuraDB Cypher queries (about 7-10 times slower). Also, we tried to rewrite the queries to Gremlin and test performance but it is still very slow. We have node and lookup indexes on AuraDB but we can't create them on AWS Neptune as it handles them automatically.

Is there any way to reach better performance on AWS Neptune?

UPDATE:

Example of Gremlin query: g.V().hasLabel('Member').has('address', eq('${address}')).outE('HAS').as('member_has').inV().as('token').hasLabel('Token').inE('HAS').as('other_member_has').outV().as('other_member').hasLabel('Member').where(__.select('member_has').where(neq('other_member_has'))).select('other_member', 'token').group().by(__.select('other_member').local(__.properties().group().by(__.key()).by(__.map(__.value())))).by(__.fold().project('member', 'number_of_tokens').by(__.unfold().select('other_member').choose(neq('cypher.null'), __.local(__.properties().group().by(__.key()).by(__.map(__.value()))))).by(__.unfold().select('token').count())).unfold().select(values).order().by(__.select('number_of_tokens'), desc).limit(20)

Example of Cypher query: MATCH (member:Member { address: '${address}' })-[:HAS]->(token:Token)<-[:HAS]-(other_member:Member) RETURN PROPERTIES(other_member) as member, COUNT(token) AS number_of_tokens ORDER BY number_of_tokens DESC LIMIT 20

To summarize the problem, I am trying to call some method which is annotated with @async in a synchronized method. Reason for using @async was because I wish to return the response of the post method (to reduce waiting time on client’s side) and continue doing some post processing work (resetting certain properties). However, as the POST method involve altering a graph database, I declare the method with synchronized keyword to prevent altering of the graph database while another computation is going on. The result of this seem to be that two threads are entering the synchronized method, which shouldn’t be occurring?

I'm new to Graph Database world, i just started to learn neo4j.

For example. I have a list of all video and a list of user watched video. How do I write pattern to query a list of not watched video based on specifed user id ?

I currently have a Neo4J Graph Database that stores 4 different kind of nodes that are connected via edges. Yellow/Red nodes that are connected to blue nodes and the yellow/red nodes have green nodes connected that give additional info on them.

What I want to do is to check if the selected node(either yellow or red) has a neighbor that is connected via the blue node, has a subset of common connected green nodes.

For example if I select the lower red node the upper red node would be returned as they both share the same far left green node neighbor which is a subset of the green node neighbors of the selected red node.

Example Graph Structure

I currently have the following Cypher query, where the table nodes are the yellow and red nodes and the keyNode are the green nodes. The blue node is the objectType.

I'm trying to build a webpage on which embed a visualization window for my local graph database. The graph database I'm using is Neo4j and for visualization I'm trying to use Cytoscape. Until now, I embedded a test graph in a webpage thanks to Cytoscape.js by putting some nodes and relationships directly in the JavaScript code. The only thing I still have problem with is the connection between Cytoscape and my local Neo4j Database. Which would be the best way to do this?